code: def __init__(
      self,
      q_min: float,
      q_max: float,
      random_state: Optional[Union[int, np.random.RandomState]] = None,
  ) -> None:
      from sklearn.preprocessing import RobustScaler
  
      self.q_min = q_min
      self.q_max = q_max
      self.preprocessor = RobustScaler(
          quantile_range=(self.q_min, self.q_max),
          copy=False,
      )
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py
imports: from typing import Dict, Optional, Tuple, Union
  import numpy
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformFloatHyperparameter
  from scipy import sparse
  from sklearn.exceptions import NotFittedError
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import Rescaling
  from autosklearn.pipeline.constants import DENSE, INPUT, SIGNED_DATA, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 1 to create a new RobustScalerComponent object from inherited base classes: Rescaling, AutoSklearnPreprocessingAlgorithm with example: obj = RobustScalerComponent(q_min, q_max, random_state)